New word learning occurs incidentally through exposure to language. Hypothesizing that effectiveness of contextual word learning in a second language (L2) depends on the quality of existing lexical semantic knowledge, we tested more and less proficient adult bilinguals in an incidental word learning task. One day after being exposed to rare words in an L2 (English) reading task, the bilinguals read sentences with the newly-learned words in the sentence-final position, followed by related or unrelated meaning probes. Both proficiency groups showed some learning through faster responses on related trials and a frontal N400 effect observed during probe word reading. However, word learning was more robust for the higher-proficiency group, who showed a larger semantic relatedness effect in unfamiliar contexts and a canonical N400 (central-parietal). The results suggest that the ability to learn the meanings of new words from context depends on the L2 lexical semantic knowledge of the reader.
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http://dx.doi.org/10.1080/23273798.2014.942673 | DOI Listing |
Conscious Cogn
January 2025
Humane Technology Lab, Catholic University of Sacred Heart, Milan, Italy; Applied Technology for Neuro-Psychology Lab., Istituto Auxologico Italiano IRCCS, Milan, Italy. Electronic address:
Psychedelic drugs offer valuable insights into consciousness, but disentangling their causal effects on perceptual and high-level cognition is nontrivial. Technological advances in virtual reality (VR) and machine learning have enabled the immersive simulation of visual hallucinations. However, comprehensive experimental data on how these simulated hallucinations affects high-level human cognition is lacking.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
Music generation by AI algorithms like Transformer is currently a research hotspot. Existing methods often suffer from issues related to coherence and high computational costs. To address these problems, we propose a novel Transformer-based model that incorporates a gate recurrent unit with root mean square norm restriction (TARREAN).
View Article and Find Full Text PDFSci Rep
January 2025
Nanfang College Guangzhou, Guangzhou, 510970, China.
Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. However, despite their advancements, these models fail to take full advantage of the multi-level features (e.
View Article and Find Full Text PDFBr J Dev Psychol
January 2025
Department of Psychology, Trinity University, San Antonio, Texas, USA.
This study investigates whether the context in which a word is learnt affects noun and verb learning. There is mixed evidence in studies of noun learning, and no studies of background perceptual context in verb learning. Two-, three-, and four-year-olds (n = 162) saw a novel object moved in a novel way while hearing four novel words, either nouns or verbs.
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